Forecasting the Disturbance Storm Time Index with Bayesian Deep Learning

Engineering

• Deep Learning 100%
• Magnetic Field 33%
• Experimental Result 33%
• NASA 33%
• Machine Learning Method 33%
• Mean Value 33%
• Root Mean Square Error 33%
• Uncertainty Quantification 33%
• Model Uncertainty 33%
• Epistemic Uncertainty 33%
• Aleatoric Uncertainty 33%
• Multi-Head Attention 33%

Keyphrases

• Bayesian Deep Learning 100%
• Disturbance Storm Time (Dst) Indices 100%
• Transformer 40%
• Space Science 10%
• Space Weather Research 10%
• Earth's Magnetic Field 10%
• Geomagnetic Storm 10%
• Machine Learning Techniques 10%
• Science Data 10%
• Uncertain Data 10%
• Bayesian Inference 10%
• Solar Wind Parameters 10%
• Index-based 10%
• Root Mean Square Error 10%
• Value of Time 10%
• Time Prediction 10%
• Attention Layer 10%
• Related Machines 10%
• Epistemic Uncertainty 10%
• Multi-head Attention 10%
• Aleatory Uncertainty 10%
• Novel Deep Learning Methods 10%
• Index Forecasting 10%
• Model Uncertainty Quantification 10%
• Hourly Forecasting 10%